title: Prediction of regulation relationship between protein interactions in signaling networks.
abstract: The discovery of regulation relationship of protein interactions is crucial for the mechanism research in signaling network. Bioinformatics methods can be used to accelerate the discovery of regulation relationship between protein interactions, to distinguish the activation relations from inhibition relations. In this paper, we describe a novel method to predict the regulation relations of protein interactions in the signaling network. We detected 4,417 domain pairs that were significantly enriched in the activation or inhibition dataset. Three machine learning methods, logistic regression, support vector machines(SVMs), and naïve bayes, were explored in the classifier models. The prediction power of three different models was evaluated by 5-fold cross-validation and the independent test dataset. The area under the receiver operating characteristic curve for logistic regression, SVM, and naïve bayes models was 0.946, 0.905 and 0.809, respectively. Finally, the logistic regression classifier was applied to the human proteome-wide interaction dataset, and 2,591 interactions were predicted with their regulation relations, with 2,048 in activation and 543 in inhibition. This model based on domains can be used to identify the regulation relations between protein interactions and furthermore reconstruct signaling pathways.
authors.affiliation: The College of Mechanical & Electronic Engineering and Automatization, National University of Defense Technology, 410073 Changsha, China. Electronic address: angel_nudt@126.com.
authors.forename: Wei
authors.initials: W
authors.lastname: Liu
authors.forename: Hongwei
authors.initials: H
authors.lastname: Xie
mesh_terms: D000818:Animals; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D051379:Mice; D060066:Protein Interaction Maps; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*; D051381:Rats; D015398:Signal Transduction
chemical_list: D011506:Proteins
keywords: Logistic regression; Protein interaction; Regulation relationship; Signaling network